Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging

TI Liaudat, M Mars, MA Price, M Pereyra… - RAS Techniques …, 2024 - academic.oup.com
Next-generation radio interferometers like the Square Kilometer Array have the potential to
unlock scientific discoveries thanks to their unprecedented angular resolution and …

Accelerating proximal Markov chain Monte Carlo by using an explicit stabilized method

M Pereyra, LV Mieles, KC Zygalakis - SIAM Journal on Imaging Sciences, 2020 - SIAM
We present a highly efficient proximal Markov chain Monte Carlo methodology to perform
Bayesian computation in imaging problems. Similarly to previous proximal Monte Carlo …

Efficient Bayesian computation for low-photon imaging problems

S Melidonis, P Dobson, Y Altmann, M Pereyra… - SIAM Journal on Imaging …, 2023 - SIAM
This paper studies a new and highly efficient Markov chain Monte Carlo (MCMC)
methodology to perform Bayesian inference in low-photon imaging problems, with particular …

Hopf algebra structures for the backward error analysis of ergodic stochastic differential equations

E Bronasco, A Laurent - ar** efficient Bayesian computation algorithms for imaging inverse problems is
challenging due to the dimensionality involved and because Bayesian imaging models are …

Conservative stabilized runge-kutta methods for the vlasov-fokker-planck equation

I Almuslimani, N Crouseilles - Journal of Computational Physics, 2023 - Elsevier
In this work, we aim at constructing numerical schemes, that are as efficient as possible in
terms of cost and conservation of invariants, for the Vlasov–Fokker–Planck system coupled …

Exotic aromatic B-series for the study of long time integrators for a class of ergodic SDE\MakeLowercase {s}

A Laurent, G Vilmart - Mathematics of Computation, 2020 - ams.org
We introduce a new algebraic framework based on a modification (called exotic) of aromatic
Butcher-series for the systematic study of the accuracy of numerical integrators for the …

Accelerated Bayesian imaging by relaxed proximal-point Langevin sampling

T Klatzer, P Dobson, Y Altmann, M Pereyra… - SIAM Journal on Imaging …, 2024 - SIAM
This paper presents a new accelerated proximal Markov chain Monte Carlo methodology to
perform Bayesian inference in imaging inverse problems with an underlying convex …

Explicit stabilised gradient descent for faster strongly convex optimisation

A Eftekhari, B Vandereycken, G Vilmart… - BIT Numerical …, 2021 - Springer
This paper introduces the Runge–Kutta Chebyshev descent method (RKCD) for strongly
convex optimisation problems. This new algorithm is based on explicit stabilised integrators …